
import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly.graph_objs as go


used_bikes_prize_in_india_data_csv = pd.read_csv("Used_Bikes_Prize_in_India_Data.csv")
used_bikes_prize_in_india_data_csv


used_bikes_prize_in_india_data_csv.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32648 entries, 0 to 32647
Data columns (total 8 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   bike_name   32648 non-null  object
 1   price       32648 non-null  int64 
 2   city        32648 non-null  object
 3   kms_driven  32648 non-null  int64 
 4   owner       32648 non-null  object
 5   age         32648 non-null  int64 
 6   power       32648 non-null  int64 
 7   brand       32648 non-null  object
dtypes: int64(4), object(4)
memory usage: 2.0+ MB


used_bikes_prize_in_india_data_csv.describe()


used_bikes_prize_in_india_data_csv.columns

Index(['bike_name', 'price', 'city', 'kms_driven', 'owner', 'age', 'power',
       'brand'],
      dtype='object')


data = [go.Scatter(x = used_bikes_prize_in_india_data_csv["price"],
                   y = used_bikes_prize_in_india_data_csv["power"],
                   text = used_bikes_prize_in_india_data_csv["bike_name"],
                   mode = "markers",
                   marker = dict(size = used_bikes_prize_in_india_data_csv["age"],
                                 color = used_bikes_prize_in_india_data_csv["kms_driven"],
                                 showscale = True))]


layout = go.Layout(title = "Bubble Chart of mpg DataSet",
                   xaxis = dict(title = 'Horsepower'),
                   yaxis = dict(title = 'Price of Bike'),
                   hovermode='closest')


fig = go.Figure(data, layout)


pyo.iplot(fig)


pyo.plot(fig, 
         filename = "tutorial_11 (Bubble Charts Exercise).html",
         image_width=1600,
         image_height=900,)

'tutorial_11 (Bubble Charts Exercise).html'

	bike_name	price	city	kms_driven	owner	age	power	brand
0	TVS Star City Plus Dual Tone 110cc	35000	Ahmedabad	17654	First Owner	3	110	TVS
1	Royal Enfield Classic 350cc	119900	Delhi	11000	First Owner	4	350	Royal Enfield
2	Triumph Daytona 675R	600000	Delhi	110	First Owner	8	675	Triumph
3	TVS Apache RTR 180cc	65000	Bangalore	16329	First Owner	4	180	TVS
4	Yamaha FZ S V 2.0 150cc-Ltd. Edition	80000	Bangalore	10000	First Owner	3	150	Yamaha
...	...	...	...	...	...	...	...	...
32643	Hero Passion Pro 100cc	39000	Delhi	22000	First Owner	4	100	Hero
32644	TVS Apache RTR 180cc	30000	Karnal	6639	First Owner	9	180	TVS
32645	Bajaj Avenger Street 220	60000	Delhi	20373	First Owner	6	220	Bajaj
32646	Hero Super Splendor 125cc	15600	Jaipur	84186	First Owner	16	125	Hero
32647	Bajaj Pulsar 150cc	22000	Pune	60857	First Owner	13	150	Bajaj

	price	kms_driven	age	power
count	3.264800e+04	32648.000000	32648.000000	32648.000000
mean	6.829542e+04	26344.625184	8.048211	213.511302
std	9.071860e+04	22208.527695	4.031700	134.428868
min	4.400000e+03	1.000000	1.000000	100.000000
25%	2.500000e+04	12000.000000	5.000000	150.000000
50%	4.300000e+04	20373.000000	7.000000	150.000000
75%	8.000000e+04	35000.000000	10.000000	220.000000
max	1.900000e+06	750000.000000	63.000000	1800.000000

Date 05-07-2021

Bubble Charts Exercise